Written development discussions occurring over different communication means (e.g. issue trackers, development mailing lists, or IRC chats) represent a precious source of information for developers, as well as for researchers interested to build recommender systems. Such discussions contain text having different purposes, e.g. discussing feature requests, bugs to fix etc. In this context, the manual classification or filtering of such discussions in according to their purpose would be a daunting and time-consuming task. In this demo we present DECA (Development Emails Content Analyzer), a tool which uses Natural Language Parsing to classify the content of development emails according to their purpose, identifying email fragments that can be used for specific maintenance tasks. We applied DECA on the discussions occurring on the development mailing lists related to Qt and Ubuntu projects. The results highlight a high precision (90%) and recall (70%) of DECA in classifying email content providing useful information to developers interested in accomplishing specific development tasks.